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# DATA FRAME OPERATIONS IN R

# Create data frame
# A dataset is ~ table (list of vectors)
id <- c(1,2,3)
name <- c("John", "Kirk", "AJ")
age <- c(21,27,18)
employees <- data.frame(ID=id, Name=name, Age=age)
employees

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# LOOPS IN R

# For
i <- 1
for(i in 1:10){
  print(paste("value of iterator: ", i))
}

# While
i <- 1

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# DOT PLOT
x <- c(1:100)
y <- x*x
plot(x, y)

# LINE PLOT
x <- c(1:100)
y <- x*x
plot(x, y, type="l")

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# Load lpsolve library
# install.packages('lpSolve')
library('lpSolve')

# Read player data (name, position, price, points)
players <- read.csv("http://spreadsheets.google.com/pub?key=0AvZWqry9LOMddDY3OVNLeDVNWTEtTEczbkRtUmx5SFE&hl=en&single=true&gid=0&output=csv", sep=",", header=TRUE)

# OBJECTIVE FUNCTION: Points of all players
f.obj <- players$Pts

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Player      Club        Pos   Price   Pts
A Carroll   Newcastle   STR   2.0     84
A Cole      Chelsea     DEF   7.0     92
C Tevez     Man City    STR   7.0     104
D Berbatov  Man Utd     STR   6.0     95
F Malouda   Chelsea     MID   5.5     106
G Bale      Tottenham   DEF   5.0     122
J Hart      Man City    GK    4.0     86
P Zabaleta  Man City    DEF   3.0     71
S Nasri     Arsenal     MID   4.0     121

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# 1) Stata command:
use growth_and_uptake_assumptions.dta

# Corresponding R command: 
load(file="my_data.RData")
attach(my_data)

# ------------------------------------------------

# 2) Stata command:

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foreach i in A B C D {
  forval n=1990/2000 {
    local m = 'n'-1
    # create new columns from existing ones on-the-fly
    generate pop'i''n' = pop'i''m' * (1 + trend'n')
  }
}

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# -----------------------------
# SUDOKU
# -----------------------------
# Solve a Sudoku problem in seconds

# create a text file under c:\ drive called "hard sudoku problem.txt" with following text (without # symbol) and then run the code below
#85---24--
#72------9
#--4------
#---1-7--2